The present invention relates to a new method for calculating time series forecasting parameters that includes looking forward to improve forecasting accuracy for supply chain needs beyond the next time period. In particular, the invention is directed to a method for estimating the parameters of a selected forecasting method such that the forecast is not necessarily most accurate in the next, upcoming time period but is instead optimized to give a more accurate forecast for some specific, user-selected future time period. The method is also applicable to estimate time series forecasting parameters when it is necessary to optimize a time series forecast for two or more future time intervals combined.
Organizations need to carefully plan and be prepared for their future business in order to be successful, and the required planning includes understanding supply chain complexities and predicting future behaviors and values of planning variables, such as product demand. For example, organizations must plan to have sufficient product available to meet demand while not having excess product that may quickly become xe2x80x9cstalexe2x80x9d or out dated. Outdated inventory is a serious and potentially costly problem for companies that manufacture and sell high technology and other products with shorter life spans.
One measure of organizational success used by organizations for planning purposes is revenue. Consequently, as a part of planning, organizations need to accurately estimate future income as well as carefully plan future expenses in order to calculate expected revenues. One method used to calculate expected revenues is to estimate future income from historical demand or sales information and then budget expenses from the estimate of future income. However, this method does not address the complexities of supply chains and the impact supplier characteristics can have on providing components that are later utilized to manufacture products to meet forecasted demand.
Another method to calculate expected revenues is to estimate both future income and future expenses from historical information and then evaluate the reasonableness of the estimated expenses to determine if the forecasted expenses need adjusting. Both of these approaches allow an organization to determine a relationship between income, expenses, and revenues, and of course, there are numerous other methods that are utilized by organizations for strategic planning.
Estimating income and other planning variables from past or historical information is always necessary regardless of which of the planning approaches is selected. Typically, some form of forecasting process uses the historical information to provide these estimations. As a specific example, organizations estimate future income by forecasting the future demand for each individual product. This approach is used since each product is likely to produce a different income per unit.
In addition to providing information about estimated income for the planning group of the organization, the forecasted demand provides information that is relatively consistent across the organization for other groups within the organization to use. For example, if the organization manufactures a product, the forecasted demand for each product is often used to determine the effect on the organization""s ability to manufacture sufficient products. The manufacturing group needs to determine if their manufacturing capabilities are adequate and if they have sufficient inventories of component parts to produce the potential product demand. Without adequate manufacturing capability or sufficient inventory, the organization may not be able to manufacture enough products to produce planned incomes. This would reduce income and adversely affect expected revenues.
The manufacturing group also relies on forecasted demand to allocate its resources efficiently, balance workload against the forecasted demand, and plan their operations to meet the needs the product demand places on them. Without this manufacturing planning, manufacturing may not be able to produce products in a timely manner, which would limit the availability of the product for the customer when the customer wants to purchase the product. This would also decrease sales and reduce income and adversely affect expected revenues.
Manufacturing organizations occasionally use just-in-time manufacturing. This manufacturing approach minimizes excess inventory by providing the component parts to assemble a product just in time for the assembly process. This reduces or even eliminates any component part inventory, which minimizes the risk of manufacturing components becoming outdated. However, even organizations that use the just-in-time approach must accurately determine how many and when each component will be needed. Such a determination again involves forecasting or predicting future demand and/or need that will exist in the next time period and, more likely, in the next several time periods to allow components to be ordered and delivered by a supplier.
A lead time problem often occurs when ordering component parts. Some suppliers can provide component parts with little or no notice, i.e., no or little lead time. Other component part suppliers may require the manufacturer to place orders for components several months in advance, even as much as six months in advance. The reasons behind this requirement may range from the length of time needed to make the component part to transportation delays for shipping the parts from manufacturing sites abroad and to a simple need to be able to plan their own future manufacturing effort. The varying lead-time problem is made even more significant if these long lead-time components are also very expensive.
For example, the manufacturer of some expensive components, such as computer components, may require orders be placed three months in advance and these orders may also be non-cancelable. Consequently, accurately forecasting how many of these components a company will need in three months or a time period coinciding with a supplier""s required lead time, is a very important part of effective organization planning, such as inventory management. Further in this regard, manufacturing organizations do not want to inaccurately order expensive components because having too few components will result in lost income from the product but having too many components will result in unnecessary expenses. Also, sometimes these expensive components have a short lifetime of usefulness and having excess inventory of outdated components may lead to wasted expense if these components need to be discarded.
Clearly, accurate forecasting is important to the success of a business or other organization, but a perfectly accurate forecast is not possible, even with existing forecasting software products and computer-implemented computation methods. Generally, the process of forecasting product demand includes accessing historical product demand data, analyzing the data to determine if they are suitable to use for forecasting, and then processing the historical demand data to produce demand forecasts for the future.
Existing forecasting typically begins with accessing historical product demand data. Organizations store their historical data in a variety of data sources. The data necessary for forecasting may be stored in spreadsheets, databases, data marts, or data warehouses. This data may be retrieved using OLAP (on-line analytical processing) engines, MOLAP (multiple OLAP) engines, ROLAP (relational OLAP) engines, Open Data Base Connectivity (ODBC), Java""s JDBC data connectivity and other methods that allow for data mining and analysis. Once historical demand data has been retrieved, data analysis determines if there exists data anomalies such as outliers, missing data, and the like that require adjustments to the historical data to smooth or remove these anomalies. Various smoothing methods in use today include setting missing data to zero, averaging neighboring data to correct for outliers or missing data, using time series forecasting with the preceding data to estimate the value of the outlier or missing data, or using a spline fitting method to interpolate for a value of the outlier or missing data After the historical demand data has been retrieved, and possibly adjusted, then this historical data is used with a variety of forecasting methods to estimate future product demand.
There are a number of forecasting methods available today (Makridakis, 1998) that allow organizations to predict future product demand from historical demand data. Although this discussion focuses on product demand, these forecasting methods can be applied to any form of business data. These forecasting methods are often referred to as xe2x80x9ctime series forecastingxe2x80x9d because the historical data used has been collected over time and is typically collected at evenly spaced intervals, such as hourly, daily, monthly, quarterly, and the like. Time series methods include single and double moving average methods, single exponential smoothing methods, Brown""s and Holt""s double exponential smoothing methods, Brown""s and Holt-Winter""s seasonal forecasting methods, Autoregressive Integrated Moving Average (ARIMA) methods, Box-Jenkins methods, Multivariate ARIMA (MARIMA), and neural network methods. Additionally, other mathematical methods are used to forecast future product demand. Multiple linear regression (Draper and Smith, 1981) is used to forecast information about a dependent variable, such as future demand, from information known about related independent variables. This method goes beyond the simple time series forecasting by including additional information from related independent variables and their forecasts. Variations of multiple linear regressions are also used in forecasting future information. These methods include Autoregressive regression, dynamic regression, stepwise regression, principal component regression, canonical correlation analysis and iterative regression.
Organizations typically apply some, or all, of these forecasting methods to individual historical data for each product. If the individual data are a part of a product grouping, then further forecasting is applied to the product group as well as the individual products. Forecasting grouped data produces a single point estimate for the forecast for various individual products and for the groups of products.
While forecasting is an important part of the planning process, existing forecasting approaches, and particularly, time series forecasting approaches, create a forecast that is most accurate for the next time period beyond the data being used in the calculation (i.e., known or historical data). For example, if the time or planning period used is months, then the forecast is most accurate for the upcoming month based on historical data collected for past months. Unfortunately, time series and other forecasting methods are less accurate for each subsequent time period in the future after the first future time period. This inaccuracy in future time periods has a significant impact on the planning process. For example, a manufacturer""s supply chain may be managed inefficiently when parts, involved in the manufacture of a product, need to be purchased in advance of the next time period because the forecast is inaccurate for the period used for planning purposes. Because the forecast is optimal for the next time period but planning decisions being made for time periods beyond the upcoming time period, the existing methods of forecasting fail to provide the accurate and useful information.
Consequently, there remains a need for methods of improving the accuracy of forecasting methods for time periods after the next, upcoming time period. Preferably, such forecasting methods would be adapted for using historical data similar to that currently collected by organizations, for using any or all of the existing methods of accessing such data, and for using existing methods of adjusting data anomalies. Additionally, it is preferable that such forecasting methods would be relatively simple and inexpensive to implement.
An object of the present invention is to permit better decisions for the scheduling of manufacturing processes and the planning of resources further into the future.
It is another object of the invention to enhance decision-making regarding the procurement of component parts for the manufacturing process, especially when a lead-time is imposed on the ordering of these components, to enable organizations to be more successful.
A more specific object of the invention is to produce a time series forecast analysis that is easy to explain and interpret and provides the understanding that the xe2x80x9cbestxe2x80x9d forecast possible is aligned with a selectable future time period, or time periods, beyond merely the next, upcoming time period.
These and other objects are met in accordance with the present invention by providing an improved method in which a forecasting error is calculated and then a set of parameters that produced that forecast are optimized thereby, minimizing the error for one or more future time periods. The forecasting method of the present invention is a robust system that can handle any form of statistical forecast. For example, time series forecasting may be employed and involves using historical information, combined with algorithmic methods, to create estimates for what the values of the previously measured variables, such as demand, will be in the future.
In accordance with a preferred embodiment of the present invention, a method is provided for generating a forecast that is optimal for a user-selectable, future time period or time periods. The method differs from existing forecasting methods because it disregards the accuracy of the near term forecast (i.e., the next, upcoming time period) and works with a forecast that is as optimal as possible for the time period in which a user of the method must make a decision. Further, if decisions need to be made that involve more than one future time period, the method may include the step of optimizing a weighted average of the forecasting accuracy for each selected period. In one embodiment, the weighting step is performed according to relative cost or loss that would occur for each unit of a forecasted component if the forecast for that component were inaccurate.
Because the present invention improves on the accuracy of time series forecasting, it may be useful to provide a brief discussion of this type of forecasting. Time series forecasting uses parameters to determine their mathematical form. Individuals using time series forecasting may specify these parameters or these parameters can be xe2x80x9coptimizedxe2x80x9d for a metric associated with the time series method. In this regard, metrics may be any one of several statistics or likelihood estimates associated with the time series forecasting method. Optimization is typically chosen to minimize the statistic or to maximize the likelihood estimate (i.e., minimize or maximize a given metric). The method of the invention is configured to work with all metrics typically associated with time series forecasting methods.
Obtaining time series metrics involves calculating the error between the mathematical form of the time series (i.e., the forecast equation) and the actual (i.e., historical) data throughout the historical periods. Significantly, the determination or calculation of all metrics used in time series forecasting requires use of the error term. For example, the metric calculation may use the absolute value of the error, the square of the error, likelihood estimates of the error, or any number of other transformations of the error. Generally, metrics have been determined based on errors that are calculated as the difference between the estimated forecast for the present period and the actual (historical) value for the same period.
The method of the present invention involves optimizing the parameters of the forecast form using the above expression of the metrics to thereby optimize the forecasting results. The method provides a change in the prior method in which the error was calculated by finding the difference between the estimated forecast (n) periods into the future (rather than simply the next time period) and the actual value for that future period. A significant problem that this invention addresses is for those situations where the optimal forecast for a period in the future is preferred over the optimal forecast for the present period. For example, if it is preferred that the forecast for two periods in the future (e.g., two months in the future if the actual data is recorded on a monthly basis) be optimal instead of the present period, the method of the present invention determines optimal parameters for forecasts two periods into the future. In another preferred embodiment, the method optimizes the average forecast over a number of periods in the future (e.g., optimizes the average of the Forward Looking Forecast, looking forward one, two and three periods). Although a simple average can be utilized, a weighted average over several periods is preferably used to indicate a different importance of the forecast for different periods, e.g., most (e.g., 70%) importance or weight would be two periods ahead, and the remaining (e.g., 30%) importance would be equally divided between one and three periods ahead.
According to one aspect of the invention, a method is provided for forecasting a value of a dependent variable, such as product demand, in a future time period later than the next, upcoming future time period. The method generally includes selecting a dependent variable for which a value is to be forecast, gathering historical data on values of the dependent variable and explanatory variables in prior time periods, and determining a forecasting equation based on the gathered historical data. Significantly, the method further includes the step, which may be completed by a user, of selecting a future time period that is a number of time periods beyond the next, upcoming time period. Once a future time period is selected, the forecasting method continues with calculating a forecasted value of the dependent variable for the selected future time period, then determining an error value by comparing the forecasted value with the historical data and based on the error value, modifying the forecasting equation to reduce the error value. In a preferred embodiment, the forecasting equation is a time series forecasting equation and the determining of the forecasting equation includes initial setting values for included time series forecasting parameters. The modifying of forecast equation then includes adjusting these forecasting parameters to lower or otherwise optimize the error value. In another preferred embodiment, the method includes selecting an error metric for optimization for the forecasting equation and the adjusting of the parameters is performed as a function of the selected error metric to move it toward an optimal value. The error metric may be selected from myriad error metrics but is typically selected from the error metrics including mean absolute deviation (MAD), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The forecasting may optionally further include prior to the calculating, cleansing the gathered historical data by identifying and replacing missing data and identifying and modifying outliers.
Other features and advantages of the invention will be seen as the following description of particular embodiments progresses, in conjunction with the drawings.